Open-source tools for geotechnical design – Eurocode 7 pile design according to Belgian practice

Geotechnical calculations are often semi-empirical algorithms where geotechnical data and subject matter expertise are combined to arrive at an estimate of foundation capacity, stiffness or installation behaviour.

Implementing geotechnical calculation algorithms is a process which is often repeated by several engineers, resulting in differences in the final outcome. Programming errors can never be entirely ruled out and algorithm implementation still require choices to be made which may not necessarily be mentioned in the codes and standards defining the algorithm.

If the program is implemented as a closed-source tool, the choices made by the programmer may never be fully know to the end user. Thus, discussion on these choices is not possible and iterative improvement of the software is halted.

On 11 March 2020, a workshop on digitisation in geotechnics was organised by the Flemish Engineering Society ie-net. In this workshop, I demonstrated the benefits of open-source implementation of geotechnical calculation tools in terms of automation and co-creation. As an example, I chose the algorithm for axial pile design according to the Belgian Annex of Eurocode 7 using the pile end bearing calculation algorithm according to De Beer (1974). This algorithm was developed at the Civil Engineering Department of Ghent University and follows a rigorous approach for scaling cone tip resistance to the unit end bearing resistance of a pile.

The code can be combined with data retrieval apps such as pydov, the Python package for automatic data extraction from Databank Ondergrond Vlaanderen to create pile capacity maps with a couple of lines of code:

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The source code is available as a GitHub repository:

https://github.com/snakesonabrain/methodedebeer

Extensive documentation is also provided:

https://www.snakesonabrain.com/docs/methodedebeer/

About Snakesonabrain

Working on the crossroads of engineering, software development and data science. I love efficiency and clean, transparant calculations. I fell in love with Python and like to share knowledge, so have a look around and let me know what you think.

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